class_docstr: Class to imitate gensim's KeyedVectors, but instead getting the vectors from the memory,
  the vectors will be retrieved from a redis db.
  Based on https://engineering.talentpair.com/serving-word-vectors-
  for-distributed-computations-c5065cbaa02f
class_docstr_tok: ["Class","to","imitate","gensim","'s","KeyedVectors",",","but","instead","getting","the","vectors","from","the","memory",",","the","vectors","will","be","retrieved","from","a","redis","db",".","Based","on","https",":","\/\/engineering.talentpair.com\/serving-word-vectors-","for-distributed-computations-c5065cbaa02f"]
code: def __init__(
      self, uri, key="", max_lru_cache_size=1024, idf_weighting="naive"
  ):
      self.word_vec = lru_cache(maxsize=max_lru_cache_size)(
          self.word_vec
      )
      self.key = f"w2v_{key}"
      self.idf_weighting = idf_weighting
  
      try:
          host, port, database = self._parse_uri(uri)
          self._redis = Redis(host, port, database)
      except RedisError as exception:
          # pylint: disable=raise-missing-from
          raise RedisKeyedVectorException(
              f"The connection to Redis failed while trying to "
              f"initiate the client. Redis error message: "
              f"{exception}"
          )
  
file_path: textpipe/textpipe/wrappers.py
imports: import pickle
  from functools import lru_cache
  from urllib.parse import urlparse
  import numpy
  from gensim.models.keyedvectors import KeyedVectors
  from redis import Redis
  from redis.exceptions import RedisError
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new RedisKeyedVectors object  with example: obj = RedisKeyedVectors(uri, key, max_lru_cache_size, idf_weighting)